Importing the required libraries

#!pip install plotly
# !pip install sort-dataframeby-monthorweek
# !pip install sorted_months_weekdays
# !pip install cufflinks
!pip install chart_studio

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Collecting retrying>=1.3.3
  Downloading retrying-1.3.4-py3-none-any.whl (11 kB)
Requirement already satisfied: plotly in c:\users\hp\anaconda3\lib\site-packages (from chart_studio) (5.14.1)
Requirement already satisfied: requests in c:\users\hp\anaconda3\lib\site-packages (from chart_studio) (2.29.0)
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Requirement already satisfied: idna<4,>=2.5 in c:\users\hp\anaconda3\lib\site-packages (from requests->chart_studio) (3.4)
Installing collected packages: retrying, chart_studio
Successfully installed chart_studio-1.1.0 retrying-1.3.4

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import sort_dataframeby_monthorweek as sm
import warnings

import cufflinks as cf
import chart_studio.plotly as py
import plotly.express as px
import plotly.graph_objects as go
import plotly.figure_factory as ff
import plotly.io as pio
from plotly.offline import init_notebook_mode
warnings.filterwarnings('ignore')

pio.renderers.default = "notebook_connected"
init_notebook_mode(connected=False)
cf.go_offline()

Loading the dataset

df = pd.read_csv('hotel_bookings.csv')

df.head()

	hotel	is_canceled	lead_time	arrival_date_year	arrival_date_month	arrival_date_week_number	arrival_date_day_of_month	stays_in_weekend_nights	stays_in_week_nights	adults	...	deposit_type	agent	company	days_in_waiting_list	customer_type	adr	required_car_parking_spaces	total_of_special_requests	reservation_status	reservation_status_date
0	Resort Hotel	0	342	2015	July	27	1	0	0	2	...	No Deposit	NaN	NaN	0	Transient	0.0	0	0	Check-Out	2015-07-01
1	Resort Hotel	0	737	2015	July	27	1	0	0	2	...	No Deposit	NaN	NaN	0	Transient	0.0	0	0	Check-Out	2015-07-01
2	Resort Hotel	0	7	2015	July	27	1	0	1	1	...	No Deposit	NaN	NaN	0	Transient	75.0	0	0	Check-Out	2015-07-02
3	Resort Hotel	0	13	2015	July	27	1	0	1	1	...	No Deposit	304.0	NaN	0	Transient	75.0	0	0	Check-Out	2015-07-02
4	Resort Hotel	0	14	2015	July	27	1	0	2	2	...	No Deposit	240.0	NaN	0	Transient	98.0	0	1	Check-Out	2015-07-03

5 rows × 32 columns

df.shape

(119390, 32)

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 119390 entries, 0 to 119389
Data columns (total 32 columns):
 #   Column                          Non-Null Count   Dtype  
---  ------                          --------------   -----  
 0   hotel                           119390 non-null  object 
 1   is_canceled                     119390 non-null  int64  
 2   lead_time                       119390 non-null  int64  
 3   arrival_date_year               119390 non-null  int64  
 4   arrival_date_month              119390 non-null  object 
 5   arrival_date_week_number        119390 non-null  int64  
 6   arrival_date_day_of_month       119390 non-null  int64  
 7   stays_in_weekend_nights         119390 non-null  int64  
 8   stays_in_week_nights            119390 non-null  int64  
 9   adults                          119390 non-null  int64  
 10  children                        119386 non-null  float64
 11  babies                          119390 non-null  int64  
 12  meal                            119390 non-null  object 
 13  country                         118902 non-null  object 
 14  market_segment                  119390 non-null  object 
 15  distribution_channel            119390 non-null  object 
 16  is_repeated_guest               119390 non-null  int64  
 17  previous_cancellations          119390 non-null  int64  
 18  previous_bookings_not_canceled  119390 non-null  int64  
 19  reserved_room_type              119390 non-null  object 
 20  assigned_room_type              119390 non-null  object 
 21  booking_changes                 119390 non-null  int64  
 22  deposit_type                    119390 non-null  object 
 23  agent                           103050 non-null  float64
 24  company                         6797 non-null    float64
 25  days_in_waiting_list            119390 non-null  int64  
 26  customer_type                   119390 non-null  object 
 27  adr                             119390 non-null  float64
 28  required_car_parking_spaces     119390 non-null  int64  
 29  total_of_special_requests       119390 non-null  int64  
 30  reservation_status              119390 non-null  object 
 31  reservation_status_date         119390 non-null  object 
dtypes: float64(4), int64(16), object(12)
memory usage: 29.1+ MB

There are total 32 variables(categorical and continuous) with one dependent variable(categorical) is_cancelled

Data cleaning

df.isnull().sum()

hotel                                  0
is_canceled                            0
lead_time                              0
arrival_date_year                      0
arrival_date_month                     0
arrival_date_week_number               0
arrival_date_day_of_month              0
stays_in_weekend_nights                0
stays_in_week_nights                   0
adults                                 0
children                               4
babies                                 0
meal                                   0
country                              488
market_segment                         0
distribution_channel                   0
is_repeated_guest                      0
previous_cancellations                 0
previous_bookings_not_canceled         0
reserved_room_type                     0
assigned_room_type                     0
booking_changes                        0
deposit_type                           0
agent                              16340
company                           112593
days_in_waiting_list                   0
customer_type                          0
adr                                    0
required_car_parking_spaces            0
total_of_special_requests              0
reservation_status                     0
reservation_status_date                0
dtype: int64

# Dropping columns having high number of missing values
df = df.drop(columns = ['agent','company'],axis=1)

# country has got 488 rows with null values, so we just replace these null values with Unknown
df = df.fillna({'country':'Unknown'})

# Coverting reservation_status_date into a datetime data type
df['reservation_status_date'] = pd.to_datetime(df['reservation_status_date'])

df.describe(include='object')

	hotel	arrival_date_month	meal	country	market_segment	distribution_channel	reserved_room_type	assigned_room_type	deposit_type	customer_type	reservation_status
count	119390	119390	119390	119390	119390	119390	119390	119390	119390	119390	119390
unique	2	12	5	178	8	5	10	12	3	4	3
top	City Hotel	August	BB	PRT	Online TA	TA/TO	A	A	No Deposit	Transient	Check-Out
freq	79330	13877	92310	48590	56477	97870	85994	74053	104641	89613	75166

for col in df.describe(include='object'):
    print(col)
    print(df[col].unique())
    print('-'*60)

hotel
['Resort Hotel' 'City Hotel']
------------------------------------------------------------
arrival_date_month
['July' 'August' 'September' 'October' 'November' 'December' 'January'
 'February' 'March' 'April' 'May' 'June']
------------------------------------------------------------
meal
['BB' 'FB' 'HB' 'SC' 'Undefined']
------------------------------------------------------------
country
['PRT' 'GBR' 'USA' 'ESP' 'IRL' 'FRA' 'Unknown' 'ROU' 'NOR' 'OMN' 'ARG'
 'POL' 'DEU' 'BEL' 'CHE' 'CN' 'GRC' 'ITA' 'NLD' 'DNK' 'RUS' 'SWE' 'AUS'
 'EST' 'CZE' 'BRA' 'FIN' 'MOZ' 'BWA' 'LUX' 'SVN' 'ALB' 'IND' 'CHN' 'MEX'
 'MAR' 'UKR' 'SMR' 'LVA' 'PRI' 'SRB' 'CHL' 'AUT' 'BLR' 'LTU' 'TUR' 'ZAF'
 'AGO' 'ISR' 'CYM' 'ZMB' 'CPV' 'ZWE' 'DZA' 'KOR' 'CRI' 'HUN' 'ARE' 'TUN'
 'JAM' 'HRV' 'HKG' 'IRN' 'GEO' 'AND' 'GIB' 'URY' 'JEY' 'CAF' 'CYP' 'COL'
 'GGY' 'KWT' 'NGA' 'MDV' 'VEN' 'SVK' 'FJI' 'KAZ' 'PAK' 'IDN' 'LBN' 'PHL'
 'SEN' 'SYC' 'AZE' 'BHR' 'NZL' 'THA' 'DOM' 'MKD' 'MYS' 'ARM' 'JPN' 'LKA'
 'CUB' 'CMR' 'BIH' 'MUS' 'COM' 'SUR' 'UGA' 'BGR' 'CIV' 'JOR' 'SYR' 'SGP'
 'BDI' 'SAU' 'VNM' 'PLW' 'QAT' 'EGY' 'PER' 'MLT' 'MWI' 'ECU' 'MDG' 'ISL'
 'UZB' 'NPL' 'BHS' 'MAC' 'TGO' 'TWN' 'DJI' 'STP' 'KNA' 'ETH' 'IRQ' 'HND'
 'RWA' 'KHM' 'MCO' 'BGD' 'IMN' 'TJK' 'NIC' 'BEN' 'VGB' 'TZA' 'GAB' 'GHA'
 'TMP' 'GLP' 'KEN' 'LIE' 'GNB' 'MNE' 'UMI' 'MYT' 'FRO' 'MMR' 'PAN' 'BFA'
 'LBY' 'MLI' 'NAM' 'BOL' 'PRY' 'BRB' 'ABW' 'AIA' 'SLV' 'DMA' 'PYF' 'GUY'
 'LCA' 'ATA' 'GTM' 'ASM' 'MRT' 'NCL' 'KIR' 'SDN' 'ATF' 'SLE' 'LAO']
------------------------------------------------------------
market_segment
['Direct' 'Corporate' 'Online TA' 'Offline TA/TO' 'Complementary' 'Groups'
 'Undefined' 'Aviation']
------------------------------------------------------------
distribution_channel
['Direct' 'Corporate' 'TA/TO' 'Undefined' 'GDS']
------------------------------------------------------------
reserved_room_type
['C' 'A' 'D' 'E' 'G' 'F' 'H' 'L' 'P' 'B']
------------------------------------------------------------
assigned_room_type
['C' 'A' 'D' 'E' 'G' 'F' 'I' 'B' 'H' 'P' 'L' 'K']
------------------------------------------------------------
deposit_type
['No Deposit' 'Refundable' 'Non Refund']
------------------------------------------------------------
customer_type
['Transient' 'Contract' 'Transient-Party' 'Group']
------------------------------------------------------------
reservation_status
['Check-Out' 'Canceled' 'No-Show']
------------------------------------------------------------

df.describe()

	is_canceled	lead_time	arrival_date_year	arrival_date_week_number	arrival_date_day_of_month	stays_in_weekend_nights	stays_in_week_nights	adults	children	babies	is_repeated_guest	previous_cancellations	previous_bookings_not_canceled	booking_changes	days_in_waiting_list	adr	required_car_parking_spaces	total_of_special_requests
count	119390.000000	119390.000000	119390.000000	119390.000000	119390.000000	119390.000000	119390.000000	119390.000000	119386.000000	119390.000000	119390.000000	119390.000000	119390.000000	119390.000000	119390.000000	119390.000000	119390.000000	119390.000000
mean	0.370416	104.011416	2016.156554	27.165173	15.798241	0.927599	2.500302	1.856403	0.103890	0.007949	0.031912	0.087118	0.137097	0.221124	2.321149	101.831122	0.062518	0.571363
std	0.482918	106.863097	0.707476	13.605138	8.780829	0.998613	1.908286	0.579261	0.398561	0.097436	0.175767	0.844336	1.497437	0.652306	17.594721	50.535790	0.245291	0.792798
min	0.000000	0.000000	2015.000000	1.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	-6.380000	0.000000	0.000000
25%	0.000000	18.000000	2016.000000	16.000000	8.000000	0.000000	1.000000	2.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	69.290000	0.000000	0.000000
50%	0.000000	69.000000	2016.000000	28.000000	16.000000	1.000000	2.000000	2.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	94.575000	0.000000	0.000000
75%	1.000000	160.000000	2017.000000	38.000000	23.000000	2.000000	3.000000	2.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	126.000000	0.000000	1.000000
max	1.000000	737.000000	2017.000000	53.000000	31.000000	19.000000	50.000000	55.000000	10.000000	10.000000	1.000000	26.000000	72.000000	21.000000	391.000000	5400.000000	8.000000	5.000000

ADR has max value of 5400 which is extremely high, probably it's an outlier

df['adr'].plot(kind='box')

<Axes: >

As we can observe that there is just one data point that is above 5000, which is an outlier. So we can just remove this outlier

df = df[df['adr']<5000]

Exploratory Data Analysis (EDA)

df['is_canceled'].value_counts(normalize=True)

0    0.629589
1    0.370411
Name: is_canceled, dtype: float64

df['is_canceled'].value_counts()

0    75166
1    44223
Name: is_canceled, dtype: int64

• ### 44223 bookings were canceled which is around 37%

df['country'].value_counts(normalize=True)

PRT    0.406981
GBR    0.101592
FRA    0.087236
ESP    0.071765
DEU    0.061036
         ...   
DJI    0.000008
BWA    0.000008
HND    0.000008
VGB    0.000008
NAM    0.000008
Name: country, Length: 178, dtype: float64

• ### Out of the total bookings 40% of the bookings were booked by guests from Portugal.

df['arrival_date_month'].value_counts(normalize=True)*100

August       11.623349
July         10.604830
May           9.876119
October       9.347595
April         9.288125
June          9.162486
September     8.801481
March         8.202598
February      6.757742
November      5.690642
December      5.678915
January       4.966119
Name: arrival_date_month, dtype: float64

• ### August has been the most busy month with 11.6% bookings followed by July (10.6%) and May (9.87%)

df['market_segment'].value_counts(normalize=True)*100

Online TA        47.305028
Offline TA/TO    20.284951
Groups           16.593656
Direct           10.558762
Corporate         4.435082
Complementary     0.622335
Aviation          0.198511
Undefined         0.001675
Name: market_segment, dtype: float64

• ### Around 47% of the bookings were made via Online Travel Agencies and around 20% of the bookings were made via offline Travel Agencies. Less than 20% of the bookings were direct

df['customer_type'].value_counts(normalize=True)

Transient          0.750588
Transient-Party    0.210438
Contract           0.034140
Group              0.004833
Name: customer_type, dtype: float64

• ### Transient type customers have done majority of bookings

df['reservation_status'].value_counts(normalize=True)

Check-Out    0.629589
Canceled     0.360301
No-Show      0.010110
Name: reservation_status, dtype: float64

• ### Around 63% of guests were checked out and 36% of guests had canceled the booking

Data Visualization

fig = px.bar(df,x=['Not canceled','canceled'],y=df['is_canceled'].value_counts(normalize=True)*100,template='plotly_dark',title='Bookings Status Count')
fig.update_layout(xaxis_title='Bookings Status',yaxis_title='Percentage of bookings')
fig.show()

• ### Majority of the bookings weren't canceled
• ### More than half of the bookings were canceled, which is still high

fig1 = px.histogram(df,x='hotel',color='is_canceled',barmode='group',template='plotly_dark')
fig1.update_layout(
    title="Cancellation rates in Resort and City Hotels",
    title_font=dict(size=20),
    xaxis=dict(title="Hotel"),
    yaxis=dict(title="count")
)
fig1.show()

• ### Most of the bookings were done in City Hotels
• ### Cancellation rate of city hotels is high compared to Resort hotels

How does the prices per night vary across different seasons or months of the year?

df['month'] = df['reservation_status_date'].dt.month
month_mapping = {
    1: 'January',
    2: 'February',
    3: 'March',
    4: 'April',
    5: 'May',
    6: 'June',
    7: 'July',
    8: 'August',
    9: 'September',
    10: 'October',
    11: 'November',
    12: 'December'
}
df['month'] = df['month'].map(month_mapping)
df = sm.Sort_Dataframeby_Month(df,'month')

room_prices_monthly = df[df['is_canceled']==0]
months = ["January", "February", "March", "April", "May", "June", 
       "July", "August", "September", "October", "November", "December"]
room_prices_monthly['month'] = pd.Categorical(room_prices_monthly['month'],categories=months,ordered=True)
resort_data = room_prices_monthly[room_prices_monthly['hotel']=='Resort Hotel']
resort_data = pd.DataFrame(resort_data.groupby(['month'])['adr'].mean().reset_index())

city_data = room_prices_monthly[room_prices_monthly['hotel']=='City Hotel']
city_data = pd.DataFrame(city_data.groupby(['month'])['adr'].mean().reset_index())

final_data = resort_data.merge(city_data,on='month')
final_data.columns = ['month','resort_hotel_prices','city_hotel_prices']
plt.figure(figsize=(10,5))
fig = px.line(final_data,x='month',y=['resort_hotel_prices','city_hotel_prices'],title = 'Room price per night over the Months', template = 'plotly_dark')
fig.update_layout(xaxis=dict(
        tickangle=45),
        yaxis_title = 'Prices')
fig.show()

<Figure size 1000x500 with 0 Axes>

• ### The prices of the Resort hotels are more expensive during summers.
• ### The prices of the City hotels varies less and are more expensive during spring and summers season.

Peak booking periods

data_guests = df[df['is_canceled']==0]
data_guests = data_guests[['month','hotel']]

data_resort = data_guests[data_guests['hotel']=='Resort Hotel']
resort_guests = data_resort['month'].value_counts().reset_index()
resort_guests.columns=['month','no_of_guests']
resort_guests

	month	no_of_guests
0	August	3268
1	July	2977
2	October	2647
3	May	2546
4	April	2529
5	March	2434
6	February	2240
7	September	2238
8	November	2157
9	January	2090
10	June	2032
11	December	1780

data_city = data_guests[data_guests['hotel']=='City Hotel']
city_guests = data_city['month'].value_counts().reset_index()
city_guests.columns=['month','no_of_guests']
city_guests

	month	no_of_guests
0	August	5293
1	July	4760
2	May	4620
3	September	4489
4	October	4351
5	June	4334
6	April	4000
7	March	3976
8	February	2900
9	November	2882
10	January	2530
11	December	2093

final_guests = resort_guests.merge(city_guests,on='month')
final_guests.columns=['month','no_of_guests_in_resort','no_of_guests_in_city_hotel']
final_guests

	month	no_of_guests_in_resort	no_of_guests_in_city_hotel
0	August	3268	5293
1	July	2977	4760
2	October	2647	4351
3	May	2546	4620
4	April	2529	4000
5	March	2434	3976
6	February	2240	2900
7	September	2238	4489
8	November	2157	2882
9	January	2090	2530
10	June	2032	4334
11	December	1780	2093

final_guests = sm.Sort_Dataframeby_Month(final_guests,'month')
final_guests

	month	no_of_guests_in_resort	no_of_guests_in_city_hotel
0	January	2090	2530
1	February	2240	2900
2	March	2434	3976
3	April	2529	4000
4	May	2546	4620
5	June	2032	4334
6	July	2977	4760
7	August	3268	5293
8	September	2238	4489
9	October	2647	4351
10	November	2157	2882
11	December	1780	2093

px.line(final_guests, x = 'month', y = ['no_of_guests_in_resort','no_of_guests_in_city_hotel'],
        title='Total no. of guests per Month', template = 'plotly_dark')

• ### There are more no. of guests in City Hotels during summers, when the prices are highest
• ### No. of guests in Resort Hotels are higher in spring and summers,when the prices are highest.
• ### Despite of the that both the hotels have fewer guests in November and December

Cancellation Status of repeated guests

fig = px.histogram(df, x="is_canceled", color="is_repeated_guest", barmode="group",template='plotly_dark')
fig.update_layout(
    title="Canceled or not",
    title_font=dict(size=20),
    xaxis=dict(title="Cancellation Status"),
    yaxis=dict(title="Count")
    
)
fig.show()

• ### This plot clearly shows that most of the repeated guests don't cancel their bookings but there are some exceptions.

Cancellation Status per month

plt.figure(figsize=(10,5))
ax = sns.countplot(x='month',hue='is_canceled',data=df,palette='Blues')
plt.title('Reservation status per month')
plt.xlabel('month')
plt.ylabel('No. of reservations')
plt.xticks(rotation=45)
plt.legend(['not canceled','canceled'])
plt.show()

• ### Both the no. of confirmed reservations and canceled reservations are largest in the month of August
• ### However Janauary is the month with most no. of canceled reservations

Top 10 countries with reservation canceled

# Assuming the 'df' variable is a pandas DataFrame
canceled_data = df[df['is_canceled'] == 1]
top_10_country = canceled_data['country'].value_counts()[:10].reset_index()
top_10_country.columns = ['country','No. of guests']

fig = px.pie(top_10_country,names='country',values='No. of guests',title='Top 10 countries with reservation canceled',template='simple_white')
fig.update_traces(textposition='inside',textinfo='label+value+percent')
fig.show()

fig = px.choropleth(top_10_country, locations='country',color='No. of guests',
                    title='Top 10 Countries with Bookings Canceled',
                    color_continuous_scale='Viridis',
                    labels={'country': 'Country', 'No. of guests': 'cancellation_count'})

fig.show()

• ### Portugal has got highest cancellation rate

Average Daily Rate (ADR) per Month for Canceled Bookings

df['month'] = df['reservation_status_date'].dt.month
month_mapping = {
    1: 'January',
    2: 'February',
    3: 'March',
    4: 'April',
    5: 'May',
    6: 'June',
    7: 'July',
    8: 'August',
    9: 'September',
    10: 'October',
    11: 'November',
    12: 'December'
}
df['month'] = df['month'].map(month_mapping)
adr_per_month = pd.DataFrame(df[df['is_canceled']==1].groupby(['month'])[['adr']].sum().reset_index())
adr_per_month = sm.Sort_Dataframeby_Month(adr_per_month,'month')
adr_per_month

	month	adr
0	January	571261.17
1	February	447915.76
2	March	424985.36
3	April	425413.23
4	May	391438.08
5	June	359688.89
6	July	482430.62
7	August	325786.93
8	September	275528.33
9	October	331156.87
10	November	281367.90
11	December	319569.53

fig = px.bar(adr_per_month,'month','adr',barmode='group',template='plotly_dark')
fig.update_layout(
    title="Average Daily Rate (ADR) per Month for Canceled Bookings",
    xaxis = dict(title="month",tickangle=-30),
    yaxis_title="Average Daily Rate (ADR) per Month"  
)
fig.show()

• ### This plot clearly shows that cancellations are common when the prices are higher and least common when the prices are lower. The prices are higher in January that leades to high no. of cancelations

Bookings by market segments

market_segment_data = pd.DataFrame(df['market_segment'].value_counts().reset_index(name='count'))
market_segment_data.columns=['market_segment','Count']
market_segment_data

	market_segment	Count
0	Online TA	56477
1	Offline TA/TO	24218
2	Groups	19811
3	Direct	12606
4	Corporate	5295
5	Complementary	743
6	Aviation	237
7	Undefined	2

px.bar(market_segment_data,x='market_segment',y='Count',template='plotly_dark')

distribution_channel_data = pd.DataFrame(df['distribution_channel'].value_counts().reset_index(name='count'))
distribution_channel_data.columns=['distribution_channel','Count']
distribution_channel_data

	distribution_channel	Count
0	TA/TO	97869
1	Direct	14645
2	Corporate	6677
3	GDS	193
4	Undefined	5

px.bar(distribution_channel_data,x='distribution_channel',y='Count',template='plotly_dark')

• ### Both the plots clearly show that most of the bookings are from travel agencies (offline/online)

Suggestions

• ### Since around 37% of the bookings were canceled, it's important to implement strategies to reduce cancellations. This can include lowering the rates to for specific hotels based on the locations. They can also provide discounts to the customers

• ### As the ratio of cancellation and not cancellation is higher in the resort hotels than in the city hotels. So the hotels should provide resonable prices during weekends or on vacations or holidays.

• ### As the number of cancellations are highest in January, hotels can start their marking with a resonable amount to increase their total revenue.

• ### Implementing marketing campaigns, tailor promotional offers or packages specifically for Portuguese guests, in order to reduce the cancellation rate.